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Bike Demand Prediction
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This project aims to build a multiple linear regression model to predict bike-sharing demand using various factors, such as season, weather conditions, temperature, and humidity. By understanding these influencing factors, BoomBikes can optimize bike availability and enhance customer satisfaction.

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Project Description
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The goal of this project is to develop a model that accurately predicts daily bike rentals, helping BoomBikes address user demand by considering patterns in weather, temperature, and seasonal factors.

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Files
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- `bike_demand_prediction.ipynb`: Contains the code for data analysis, model training, and evaluation.
- `day.csv`: The dataset with daily bike-sharing information, including fields like season, weather, and daily rental counts.

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Dataset Characteristics
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day.csv have the following fields:
	
	- instant: record index
	- dteday : date
	- season : season (1:spring, 2:summer, 3:fall, 4:winter)
	- yr : year (0: 2018, 1:2019)
	- mnth : month ( 1 to 12)
	- holiday : weather day is a holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
	- weekday : day of the week
	- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
	+ weathersit : 
		- 1: Clear, Few clouds, Partly cloudy, Partly cloudy
		- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
		- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
		- 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
	- temp : temperature in Celsius
	- atemp: feeling temperature in Celsius
	- hum: humidity
	- windspeed: wind speed
	- casual: count of casual users
	- registered: count of registered users
	- cnt: count of total rental bikes including both casual and registered

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Data Preparation
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- Converted categorical columns to descriptive labels for interpretability.
- Created dummy variables to handle categorical data.
- Split the dataset into training and testing sets to evaluate model performance.

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Model Evaluation
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The model's performance is evaluated using the R-squared metric to measure prediction accuracy, helping to identify the most influential factors on bike demand.

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Contact
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For further information about this dataset please contact Prem Chavan([email protected])